M.S. is supported by a full PhD scholarship from GERLS-DAAD program. We would like to thank Dr. Andrew Wiszniewski for proof reading and commenting on the manuscript. We are very grateful to all the members of the Giavalisco lab at Max-Planck Institute of Molecular Plant Physiology, Golm, Germany for their help.

Caution: Methanol (MeOH) and methyl tert-butyl ether (MTBE), used during extraction, are flammable, and can have, on prolonged exposure and/or contact, respiratory, eye or skin irritation. Please handle them carefully only in a fume hood and use the appropriate safety procedures during the extraction (lab coat, safety glasses, gloves, etc.). Liquid nitrogen and dry ice, used in several steps of this protocol, can cause severe burns by prolonged skin contact. Please handle them carefully by wearing protective gloves and glasses. Users can use different chemicals, reagents or internal standards for sample analysis, some of which may be toxic. Please examine the relevant chemical safety data sheets for all material used.
1. Collection and Harvesting of Biological Samples
<ol>
	<li>Prepare labelled harvesting tubes. 
	NOTE: Here, harvest biological samples in labelled, 2 mL, round-bottom, safe lock microcentrifuge tubes containing two 5 mm diameter, metal balls for the tissue homogenizer.</li>
	<li>Prepare a filled liquid nitrogen dewar.</li>
	<li>Harvest the biological sample and snap freeze the tissue in liquid nitrogen. Perform this step as quickly as possible within few seconds to avoid metabolic changes induced by wounding. 
	Note: For demonstration purposes, use rosette leaves from 30 day-old wild-type Arabidopsis thaliana (Col-0) grown on soil under long day conditions.</li>
	<li>Keep the harvested samples on dry ice for short-term breaks or store them at -80 &#176;C for longer periods.</li>
</ol>
2. Grinding and Tissue Disruption
<ol>
	<li>Pre-cool the tube holders of the tissue homogenizer in liquid nitrogen for at least 10 min. If a tissue homogenizer is not available, use clean and pre-cooled mortars and pestles.</li>
	<li>Take the samples from the liquid nitrogen, dry ice or -80 &#176;C freezer and place them in the pre-cooled tube holders.</li>
	<li>Quickly put the tube holders in the tissue homogenizer.</li>
	<li>Grind the biological material into a fine and homogeneous powder. Use 20 Hz for 1 min for leaves. 
	Note; Homogenization time and speed may be varied depending on the tissue, make sure that the sample is homogenized into a fine powder and that this powder is kept frozen at every step of the homogenization.</li>
	<li>Take the biological samples from the tube holders and keep them frozen until further extraction.</li>
</ol>
3. Weighing of Tissues
<ol>
	<li>Use an analytical balance with sufficient precision for the required sample amounts.</li>
	<li>Prepare a labelled 2 mL round bottom safe-lock microcentrifuge tube.</li>
	<li>Pre-cool the tubes and spatulas in liquid nitrogen.</li>
	<li>Aliquot the required amount of tissue powder into the 2 mL safe-lock microcentrifuge tube. 
	Caution: Avoid any defrosting of the plant material by minimizing the time taken to weigh the samples.</li>
	<li>Return the aliquoted samples immediately after weighing to liquid nitrogen.</li>
	<li>Record for each sample the exact weight. Use 10-50 mg &#177; 10% for most plant tissues.</li>
	<li>Store the aliquoted samples at -80 &#176;C until further extraction.</li>
</ol>
4. Reagent Setup
<ol>
	<li>Use an extraction mixture of methyl tert-butyl ether (MTBE)/ methanol (MeOH).
	<ol>
		<li>For the preparation of 100 mL extraction solvent, add 75 mL of MTBE to 25 mL of MeOH to make a mixture of MTBE: MeOH (3:1, vol/vol).</li>
		<li>Add internal standards for the post analysis normalization according to analytical needs. Typically, add 50 &#181;L of 1,2-diheptadecanoyl-sn-glycero-3-phosphocholine (1 mg/mL in chloroform) as an internal standard for the UPLC-MS-based lipid analysis, while adding 50 &#181;L of 13C sorbitol (1 mg/mL in water) as internal standards for the GC-MS-based analysis of primary metabolites. Internal standards for UPLC-MS-based metabolite analysis are 50 &#181;L of corticosterone (1 mg/mL in methanol) and 25 &#181;L of ampicillin (1 mg/mL in methanol).</li>
		<li>Transfer the solvent to a clean glass bottle that was rinsed with MTBE: MeOH mixture.</li>
		<li>Store the extraction mixture for up to 1 week at 4 &#176;C. 
		NOTE: Do not store the extraction mixture for longer periods to maintain reproducible results.</li>
	</ol>
	</li>
	<li>To induce phase separation, use water (H2O)/ methanol (MeOH).
	<ol>
		<li>For the preparation of 100-mL H2O: MeOH, add 75 mL of H2O to 25 mL of MeOH to make H2O: MeOH (3:1, vol/vol).</li>
		<li>Transfer the solvent to a clean glass bottle that was rinsed with H2O: MeOH mixture. This solvent can be stored for several weeks at room temperature.</li>
	</ol>
	</li>
</ol>
5. Extraction of Samples
<ol>
	<li>Pre-cool the extraction mixture (MTBE: MeOH, 3:1, vol/vol) to -20 &#176;C using a liquid cooling system or a -20 &#176;C freezer.</li>
	<li>Take out the aliquoted samples one by one and add 1 mL of the pre-cooled extraction mixture to each sample tube. Caution: Perform this step quickly due to low viscosity of MTBE.</li>
	<li>Mix immediately on a vortex mixer until the tissue is well homogenized within the extraction mixture. 
	NOTE: This step is very important, since it is required to precipitate the proteins and inactivates their enzymatic activities.</li>
	<li>Incubate all the samples on an orbital shaker at 100 rpm for 45 min at 4&#176;C.</li>
	<li>Sonicate the samples for 15 min in an ice-cooled sonication bath.</li>
</ol>
6. Fractionation by Phase Separation
<ol>
	<li>Add 650 &#181;L of H2O: MeOH (3:1, vol/vol) to each sample tube.</li>
	<li>Mix well by vortexing for 1 min.</li>
	<li>Centrifuge the samples at a speed of 20,000 x g for 5 min at 4 &#176;C. 
	NOTE: After this step, there are two immiscible liquid phases with a solid pellet in the bottom of the tube. 
	Caution: Handle the tubes with care to avoid mixing of the two liquid phases and avoid disrupting the precipitated pellet.</li>
</ol>
7. Aliquoting of Polar and Hydrophobic Fractions
<ol>
	<li>Transfer 500 &#181;L of the solvent from the upper, lipid-containing, phase into a labelled 1.5 mL microcentrifuge tube. 
	NOTE: The aliquoted lipid samples can be directly concentrated for immediate UPLC-MS analysis (step 8.1) or stored for several weeks at -80 &#176;C.</li>
	<li>Once the 500 &#181;L are removed from the sample, remove the remaining lipid phase using a 200 &#181;L pipette.</li>
	<li>Transfer 400 &#181;L of the solvent from the lower phase (polar and semi-polar metabolites) into a labelled 1.5 mL microcentrifuge tube. The aliquoted polar samples can be directly concentrated for immediate UPLC-MS analysis (step 8.2) or stored for several weeks at -80 &#176;C.</li>
	<li>Take an additional aliquot of 200 &#181;L to perform additional analysis, e.g. gas chromatography-based metabolite analysis as described preciously16.</li>
	<li>Remove the remainder of the aqueous phase by pipetting off the excess volume.</li>
	<li>Wash the obtained protein, starch, cell wall pellet with 500 &#181;L methanol by thoroughly vortexing it for 1 min.</li>
	<li>Centrifuge the samples at a speed of 10,000 x g for 5 min at 4&#176;C.</li>
	<li>Perform protein extraction and digestion (step 11) or starch/cell wall analysis as described previously16. 
	NOTE: If not utilized immediately, these pellets can be stored for several weeks at -80 &#176;C.</li>
</ol>
8. Concentration and Storage of Fractions
<ol>
	<li>Evaporate the solvent from lipid samples (from step 7.1) in either a vacuum concentrator without heating (for 1-2 hours) or preferably use a nitrogen flow evaporator to avoid oxidative modifications of the lipids. 
	NOTE: Dried samples should be analyzed immediately. For storage, leave samples in MTBE solution, ideally in glass vials (step 7.1).</li>
	<li>Evaporate the solvent from the aqueous samples (from step 7.3 or 7.4) overnight in a vacuum concentrator without heating. NOTE: the dried samples can be stored for several weeks at -80 &#176;C before analysis.</li>
</ol>
9. Analysis of Lipids using UPLC-MS24
<ol>
	<li>Re-suspend the dried lipid fractions (from step 8.1) in 400 &#181;L of acetonitrile: 2-propanol (7:3, vol/vol).</li>
	<li>Transfer sufficient liquid to glass vials and cap tightly.</li>
	<li>Put the glass vials in a cooled autosampler (4 &#176;C).</li>
	<li>Inject 2 &#181;L per sample and separate the lipids on a Reversed Phase (RP) C8 column held at 60 &#176;C using a UPLC system running at a flow rate of 400 &#181;L/min.</li>
	<li>Use the mobile phases described in Table 1 for the chromatographic separation.</li>
	<li>Acquire the mass spectra in positive and negative ionization mode using a suitable MS instrument covering the mass range between 150 and 1,500 m/z.</li>
</ol>
10. Analysis of Polar and Semi-polar Metabolites using UPLC-MS25.
<ol>
	<li>Re-suspend the polar phase (from step 8.2) in 200 &#181;L UPLC-grade methanol: water (1:1, vol/vol).</li>
	<li>Transfer sufficient liquid to glass vials and cap tightly.</li>
	<li>Put the glass vials in a cooled autosampler (4 &#176;C).</li>
	<li>Inject 2 &#181;L from each sample and separate the metabolites on a RP C18 column held at 40 &#176;C using a UPLC system running at a flow rate of 400 &#181;L/min.</li>
	<li>Use the mobile phases for chromatographic separation with the parameters given in Table 2.</li>
	<li>Acquire full scan mass spectra in positive and negative ionization mode using a suitable mass spectrometer covering a mass range between 50 and 1,500 m/z.</li>
</ol>
11. Protein Extraction, Digestion and Analysis16
<ol>
	<li>Re-suspend the washed protein/starch/cell wall pellet (from step 7.8) in 200 &#181;L of the protein extraction buffer of choice. Note: We use urea/thiourea buffer (5 M urea, 2 mM thiourea, 15 mM DTT, 2% CHAPS and protease and phosphatase inhibitors)27.</li>
	<li>Sonicate the samples for 10 min in an ice-cooled sonic bath.</li>
	<li>Incubate samples for 30 min on an orbital shaker (100 rpm) at room temperature.</li>
	<li>Centrifuge the dissolved proteins at 10,000 x g for 5 min.</li>
	<li>Collect the protein supernatant in a new tube.</li>
	<li>Determine the protein concentration from the collected supernatant28.</li>
	<li>Digest 50 &#181;g of protein in-solution with a protocol of choice. Typically, use the Trypsin/Lys-C mix according to the instruction manual.</li>
	<li>After digestion, perform desalting of the peptides prior to mass spectrometry using C18 stage tips and elute the digested peptides 29.</li>
	<li>Concentrate the samples to near dryness in a vacuum concentrator without heating.</li>
	<li>Re-suspend the samples in appropriate loading buffer (e.g. 5% acetonitrile, 0.5% formic acid) and analyze the peptide mixtures by LC-MS/MS using a high-resolution mass spectrometer connected to a nano LC system. 
	NOTE: In the exemplary proteomics data set presented in this protocol, we used a gradient as described in Table 3.</li>
	<li>Set the mass spectrometer, using a top 15 strategy, where one full scan (FS) was followed by up to 15 data dependent MS/MS scans, to the following parameters: The FS was in the mass range 200 - 2,000 m/z at a resolution of 70,000 with a target value of 3x106 ions. Obtain the data-dependent MS/MS scans by higher-energy collisional dissociation (HCD). Set the target values for the MS/MS to 1e5 ions, with a maximal ion fill time of 50 ms, an isolation window of 4.0 m/z, normalized collision energy (NCE) of 30% and an underfill ratio of 1%. Measure the MS/MS ions at a resolution 17.500 and the dynamic exclusion was set to 60 s.</li>
</ol>

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K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uric+acid+accumulation+in+an+Arabidopsis+urate+oxidase+mutant+impairs+seedling+establishment+by+blocking+peroxisome+maintenance.">Uric acid accumulation in an Arabidopsis urate oxidase mutant impairs seedling establishment by blocking peroxisome maintenance.</a> Plant Cell. 26 (7), 3090-3100 (2014).</li><li>Avin-Wittenberg, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+analysis+of+the+role+of+autophagy+in+cellular+metabolism+and+energy+homeostasis+in+Arabidopsis+seedlings+under+carbon+starvation.">Global analysis of the role of autophagy in cellular metabolism and energy homeostasis in Arabidopsis seedlings under carbon starvation.</a> Plant Cell. 27 (2), 306-322 (2015).</li><li>Watanabe, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+dissection+of+spatiotemporal+metabolic+shifts+in+primary,+secondary,+and+lipid+metabolism+during+developmental+senescence+in+Arabidopsis.">Comprehensive dissection of spatiotemporal metabolic shifts in primary, secondary, and lipid metabolism during developmental senescence in Arabidopsis.</a> Plant physiol. 162 (3), 1290-1310 (2013).</li><li>Lambers, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteaceae+from+severely+phosphorus-impoverished+soils+extensively+replace+phospholipids+with+galactolipids+and+sulfolipids+during+leaf+development+to+achieve+a+high+photosynthetic+phosphorus-use-efficiency.">Proteaceae from severely phosphorus-impoverished soils extensively replace phospholipids with galactolipids and sulfolipids during leaf development to achieve a high photosynthetic phosphorus-use-efficiency.</a> New Phytol. 196 (4), 1098-1108 (2012).</li><li>Degenkolbe, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+remodeling+of+the+lipidome+during+cold+acclimation+in+natural+accessions+of+Arabidopsis+thaliana.">Differential remodeling of the lipidome during cold acclimation in natural accessions of Arabidopsis thaliana.</a> Plant J. 70, 972-982 (2012).</li><li>Bromke, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Liquid+chromatography+high-resolution+mass+spectrometry+for+fatty+acid+profiling.">Liquid chromatography high-resolution mass spectrometry for fatty acid profiling.</a> Plant J. 81 (3), 529-536 (2015).</li><li>Li, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FAX1,+a+novel+membrane+protein+mediating+plastid+fatty+acid+export.">FAX1, a novel membrane protein mediating plastid fatty acid export.</a> PLoS Biol. 13 (2), 1002053 (2015).</li><li>Kueger, S., Steinhauser, D., Willmitzer, L., Giavalisco, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+plant+metabolomics:+from+mass+spectral+features+to+metabolites+and+from+whole-cell+analysis+to+subcellular+metabolite+distributions.">High-resolution plant metabolomics: from mass spectral features to metabolites and from whole-cell analysis to subcellular metabolite distributions.</a> Plant J. 70 (1), 39-50 (2012).</li></ol>

The authors have nothing to disclose

In this article, we describe and illustrate a simple and highly applicable extraction protocol for comprehensive lipidomic, metabolomic and proteomic analysis from a single 50 mg leaf sample. The method has been previously used in several studies, which have been published in diverse articles 17,18,19,20,21,22,23,24,25,26,31,32,33,34,35,36,37and proved, in addition to its straight-forward workflow and high applicability to be robust and reproducible.
The here provided applications show some routine methods for the initial screening of a complex biological sample. These illustrated large scale metabolomic and lipidomic data sets can provide comprehensive information about the broad or specific changes in the metabolism of the analyzed biological system, while the data obtained from the analysis of proteins, provides insights into the quantitative (abundance) and qualitative (modifications) changes of enzymes, structural proteins or transcription factors (TFs) controlling cellular functions and machinery. Accordingly, integrative omics data has the potential to reveal initial information about possible changes induced by genetic or biotic and/or abiotic perturbations of a biological system, by elucidating molecular changes of diverse molecules associated to specific metabolic pathways or cellular processes.
Of course, in the long term, it is quite essential, for a successful systems biology analysis, to maximize the number of analyzed and annotated molecular entities, allowing the monitoring of cellular functions and activities as completely as possible. For this purpose, the obtained fractions could be applied additionally to diverse analytical methods, targeting further compounds or compound classes (Figure 4).
Having said this, it has to be mentioned that the global analysis strategy of the obtained data can be following by two different strategies: on the one hand, we have been emphasizing the elucidation of cellular functions by quantification of known compounds. On the other hand, many of the measured metabolites and lipids are not yet known or annotated. These, yet, un-annotated compound measurements also contain plenty of meaningful information, which, can be utilized by statistical methods for the classification or discrimination between groups or treatments 20,21,22.
Still, these unknown compounds, especially the ones relevant for group classification or serving as biomarkers need to be identified. This identification process is unfortunately quite tedious and cannot be achieved without additional analytical measurements or strategies38. As can be seen from Figure 4, the number of un-annotated compounds is quite high (actually the vast majority). Nevertheless, as mentioned above, these chromatographic peaks can be handled within the data analysis and therefore significantly affected entities can be elucidated and subjected to further identification strategies.
In summary, we can conclude that the here introduced protocol provides several advantages for experimental systems biology as well as for classical statistical applications.
First, since all fractions are extracted from a single sample, the variation between the different experimental data sets (lipids, metabolites, proteins) is significantly reduced since every data set is derived from the same sample aliquot. This clearly leads to the increased comparability of the obtained results.
Second, the method is easily scalable and makes it therefore highly compatible with small to large sample amounts. We routinely use 10-100 mg of tissue samples, but successful lipidomic studies have also been performed on as little as 20 Arabidopsis seeds31. Especially the compatibility with small sample amounts makes this method applicable if limited amounts of biological tissues or samples are available. Still, even if sufficient sample material is available, the method presented here offers the advantage to utilize these samples in a larger number of experimental replicates instead of using them for different extraction procedures. This allows for a better and refined statistical data analysis.
Third, since the method is based on a liquid-liquid fractionation of polar and non-polar molecules, it provides, in contrast to simple one-phase extraction methods (e.g. methanol extractions) a significant de-complexing step in the procedure. This efficient sample de-complexing leads to a partial purification of the individual fractions due to the separation of chemically interfering molecules from each other. Accordingly, the chemical partitioning process not only provides a practical advantage for the systematic aliquoting of the extracted samples into different chemical classes but also improves the individual analytical measurements, since it removes contaminating compounds from the different fractions. Clearly, we can observe that especially the lipids, which are partitioned to the organic phase and which usually negatively affect the chromatographic analysis of polar compounds, will be almost completely absent from the polar fraction. The same is true for the analysis of the hydrophobic lipids, which will be depleted of the polar compounds. Besides the purification of polar and nonpolar compounds from each other, we deplete and collect proteins and other macro-molecules from the sample, which not only provides a separate fraction, which can be utilized for protein, starch and cell wall analysis16, but also leads to a cleaner sample within the individual fractions. This is especially relevant, since it is known that the presence of large macromolecules, leads to damage or at least shorter lifetime of the analytical columns.
Last but not least, the described MTBE extraction method, which relies on the less hazardous and more favorable chloroform replacement solvent15, has already been shown by several studies from our group, to be widely applicability for different biological samples from plants16, algae17,18, flies19 but also several mammalian tissues, organs or cells20,21,22.

Systems biology, which emerged in the middle of the last century1 and was advanced by the large scale analysis of genomic and transcriptomic data sets2,3, has developed into a new and indispensable approach for the analysis of complex biological systems4,5. The main goal of systems biology is to decipher the component interactions and dependencies in biological systems and to bridge the link between genotypes, their realization, molecular transformations and resulting phenotypes. Accordingly, the integration of comprehensive data sets, produced by the various large scale analytical approaches, namely genomics, transcriptomics, metabolomics, lipidomics and proteomics and their computational analysis has become a prerequisite for the description and understanding of complex biological systems.
Based on the huge chemical diversity and complexity of biological components in any living system, the production of large and comprehensive &#39;omics&#39; data sets strongly relies on the quality of the applied extraction method9. In addition to the quality of the extraction method, the economy of the method is important; this means that it would be desirable to obtain as much molecular information from as little sample input as possible. Often sample amounts can be limiting and therefore it is highly desirable to make use of an extraction method, which can derive as many molecular classes from a single extraction of a given sample. This means that instead of using several specialized extraction methods for the extraction of different compound classes from different sample aliquots of the same sample, a sequential extraction method is employed, which fractionates the molecular constituents of a single aliquot into different molecular fractions.
The common method employed for these fractionated extraction methods is based on the two phase lipid extraction method from Folch et al. developed in 195710. This method is based on a chloroform: methanol/water partitioning of polar and hydrophobic metabolites and was intended to clean-up and de-complex samples for high quality lipid analysis. With the evolution of multi-omics systems biology, the Folch method was further, stepwise, improved by utilizing it for the sample partitioning of proteins and polar metabolites and lipids for gas- and liquid chromatography-based metabolomics and lipidomics of polar and hydrophobic compounds, in addition to liquid chromatography-based proteomics11,12,13,14. Unfortunately, all of these methods rely on a chloroform-based extraction method, which not only leads to the undesired formation of the protein pellet as an interphase between the polar and the lipid phase, but which is also an undesirable solvent from a green chemistry perspective15,16. However, the solvent methyl tert-butyl ether (MTBE) overcomes both of these aforementioned problems and is a suitable replacement for chloroform. Based on these requirements, we decided to establish a MTBE: methanol : water-based extraction method, which fulfils all of the aforementioned specifications and therefore functions as an ideal starting point for comprehensive multi-omics analysis16.
This protocol guides the user step-by-step through the simple, fast and reproducible workflow of the sample preparation, including troubleshooting of commonly observed problems. Further, we will briefly introduce exemplary analytical data from ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based lipidomics, metabolomics and proteomics profiling from plant tissue samples. Even though the given examples are derived from 50 mg of an Arabidopsis thaliana leaf tissue sample, this protocol has been used for several other biological samples and tissues, including algae17,18, insects19 and mammalian cells, organs and tissue20,21,22. The scope of the presented extraction protocol is to provide a clear and detailed description of the pre-extraction sample handling and the extraction procedure itself. Even though we provide three brief examples of analytical application, detailed information about the pre- and post-analytical data handling can be obtained from our previous publications 16,23,24,25,26.

<table border="0" cellpadding="0" cellspacing="0" width="1070">
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		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:357px;">Reagents and standards</td>
			<td style="width:239px;"></td>
			<td style="width:128px;"></td>
			<td style="width:347px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ampicillin&#160;</td>
			<td>Sigma Aldrich</td>
			<td>A9393-5G</td>
			<td>Internal standard for metabolites</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Corticosterone&#160;</td>
			<td>Sigma Aldrich</td>
			<td>27840-500MG</td>
			<td>Internal standard for metabolites, HPLC grade</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">13C Sorbitol</td>
			<td>Sigma Aldrich</td>
			<td>605514</td>
			<td>Internal standard for metabolites, ISOTEC&#174;&#160;Stable Isotopes</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">1,2-diheptadecanoyl-sn-glycero-3-phosphocholine (17:0 PC)&#160;</td>
			<td>Avanti Polar Lipids</td>
			<td>850360P</td>
			<td>Internal standard for lipids</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Methanol (MeOH)&#160;</td>
			<td>Biosolve Chemicals</td>
			<td>13684102</td>
			<td>ULC-MS grade</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Water</td>
			<td>Biosolve Chemicals</td>
			<td>23214102</td>
			<td>ULC-MS grade</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Methyl tert-butyl ether (MTBE)&#160;</td>
			<td>Biosolve Chemicals</td>
			<td>13890602</td>
			<td>HPLC grade</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Trypsin/Lys-C mix&#160;</td>
			<td>Promega</td>
			<td>V5072</td>
			<td>Enzymatic digestion of proteins</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Equipment</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Balance&#160;</td>
			<td>Sartorius Corporation&#160;</td>
			<td>14 557 572</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Tissue grinding mixer mill&#160;</td>
			<td>Retsch, Mixer Mill MM 300</td>
			<td>20.746.0001</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Mortar and pestle&#160;</td>
			<td>Sigma Aldrich</td>
			<td>Z247464-1EA</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Vortex mixer&#160;</td>
			<td>Vortex-Genie 2, Model G560</td>
			<td>SI-0236</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Vacuum concentrator</td>
			<td>Scan Speed Maxi Vac Alpha Evaporators</td>
			<td>7.008.500.002</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2 ml Safe-lock microcentrifuge tubes&#160;</td>
			<td>Eppendorf</td>
			<td>30120094</td>
			<td>Used for sample extarction</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">1.5 ml Safe-lock microcentrifuge tubes&#160;</td>
			<td>Eppendorf</td>
			<td>30120086</td>
			<td>Used for fractions</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Shaker</td>
			<td>Eppendorf Thermomixer 5436</td>
			<td>2050-100-05</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Sonicator&#160;</td>
			<td>USC 300&#160;TH</td>
			<td>142-0084</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Refrigerated microcentrifuge</td>
			<td>Eppendorf, model 5427R</td>
			<td>22620701</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">UPLC system&#160;</td>
			<td colspan="2">Waters Acquity UPLC system (Waters, Machester, UK)</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">&#160;MS system&#160;</td>
			<td colspan="3">Exactive, Orbitrap-type, MS (Exactive, Thermo-Fisher, Bremen, Germany).</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Reversed Phase (RP) Bridged Ethyl Hybrid (BEH) C8 column (100 mm&#215;2.1 mm containing 1.7 &#956;m diameter particles)</td>
			<td>Waters, Machester, UK</td>
			<td>186002878</td>
			<td>Analysis of lipids</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">RP High Strength Silica (HSS) T3 column (100 mm&#215;2.1 mm containing 1.8 &#956;m diameter particles)&#160;</td>
			<td>Waters, Machester, UK</td>
			<td>186003539</td>
			<td>Analysis of metabolites</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Q ExactivePlus high resolution mass spectrometer connected to an EASY-nLC 1000 system&#160;</td>
			<td>Thermo-Fisher, Bremen, Germany</td>
			<td></td>
			<td>Analysis of peptides</td>
		</tr>
	</tbody>
</table>

a simple fractionated extraction method for the comprehensive analysis of metabolites, lipids, and proteins from a single sample

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually determined by transcriptomic, proteomic, metabolomics and lipidomic measurements. In this protocol, we introduce a simple method for the reproducible extraction of metabolites, lipids and proteins from biological tissues using a single aliquot per sample. The extraction method is based on a methyl tert-butyl ether: methanol: water system for liquid: liquid partitioning of hydrophobic and polar metabolites into two immiscible phases along with the precipitation of proteins and other macromolecules as a solid pellet. This method, therefore, provides three different fractions of specific molecular composition, which are fully compatible with common high throughput 'omics' technologies such as liquid chromatography (LC) or gas chromatography (GC) coupled to mass spectrometers. Even though the method was initially developed for the analysis of different plant tissue samples, it has proved to be fully compatible for the extraction and analysis of biological samples from systems as diverse as algae, insects, and mammalian tissues and cell cultures.

Comprehensive multi-omics data sets are invaluable for understanding of complex biological systems. The strategy for a successful biological experiment usually starts from a meaningful experimental design, experiment set-up and performance, followed by sample-collection, extraction, analytical data acquisition, raw data processing, statistical data analysis, identification of relevant metabolite and biological data interpretation including pathway mapping and visualization (Figure 1).
In the extraction protocol introduced here, we focus on the sample-collection, -handling and -extraction steps, which are depicted in the detailed workflow overview in Figure 2. For demonstration purposes, 50 mg of Arabidopsis leaf tissue was selected. This material was harvested, ground and extracted before subjecting it to three exemplary analytical UPLC-MS platforms, providing data that can be utilized for targeted and untargeted lipidomic, metabolomic and proteomic analysis. Figures 2 and 6 additionally include representative pictures of how, under standard conditions, the extraction solvent should look like. Additionally, examples of samples containing excessive amounts of precipitated macromolecules (proteins and starch) and samples with inappropriate sample homogenization are shown (Figure 3). The troubleshooting for these two common problems is given in brief in Figure 3 but it is also discussed in more detail in our previous publication16.
Figures 4 and 5 outline examples of three analytical chromatograms derived from the lipid, the polar/semi-polar metabolites and protein analyses. Lipids, which were taken from the upper MTBE phase (Figure 2), were analyzed by reversed phase (RP) C8 ultra performance liquid chromatography coupled to high-resolution mass spectrometry. Lipids can be acquired using positive and negative MS ionization modes (Figure 4, upper pane)16,24.
Polar and semi-polar primary and secondary metabolites were analyzed from the polar (water/methanol) phase (Figure 2) by reversed phase (RP) C18UPLC-MS25. The illustrated method, using the reversed phase chromatography, is highly compatible to the analysis of semi-polar metabolites (namely metabolites from plant secondary metabolism), which can be analyzed using positive and negative ionization modes in the MS (Figure 4, lower pane)16. More hydrophilic metabolites from this fraction (sugars, polar amino acids etc.), which do not show good retention on the reversed phase material, can be analyzed by other analytical methods such as GC-MS16or hydrophilic interaction liquid chromatography30.
The proteins, which were retrieved from the solid pellet in the bottom of the extraction tube (Figure 2), were in-solution digested and analyzed using shot gun LC-MS (Figure 5), while the protocol for extraction of starch and cell wall material can be obtained from our previously published protocol16.
In summary, more than 200 lipid species, 50 annotated semi-polar metabolites and several thousand proteins can be routinely identified from samples of the type used in our example. Additionally, the method showed wide applicability using different tissues, organs and cell culture material (Figure 6).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55802/55802fig1.jpg" /> 
Figure 1: General Workflow for Large Scale Untargeted Omics Analysis. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55802/55802fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55802/55802fig2.jpg" /> 
Figure 2: Sample Preparation and Extraction Workflow for the Analysis of Lipids, Metabolites and Proteins from a Single Aliquot of a Biological Sample. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55802/55802fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55802/55802fig3.jpg" /> 
Figure 3: Illustrative Examples of Commonly Observed Problems using Two-phase Partitioning Extraction Protocols. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55802/55802fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55802/55802fig4.jpg" /> 
Figure 4: Representative Chromatograms of Lipid and Semi-polar Metabolites from Arabidopsis thaliana Leaf Extracts. Base peak chromatograms of lipids (upper pane) and semi-polar metabolites (lower pane) analyzed in positive ionization mode16. The pie charts in the upper right corner of each chromatogram shows the number of identified lipids and metabolites assigned to different chemical classes. Chl, chlorophylls; DAG, diacylglyceride; DGDG, digalactosyldiacylglycerol; FA, fatty acid; LysoPC, lysophosphatidylcholine; MGDG, monogalactosyldiacylglycerol; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; SP, sphingolipid; SQDG, sulfoquinovosyldiacylglycerol; TAG, triacylglyceride. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55802/55802fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55802/55802fig5.jpg" /> 
Figure 5: Representative Base Peak Chromatogram of Peptides from Arabidopsis thaliana Leaf Extracts. 
The pie chart shows in the upper right corner indicates the number of identified proteins assigned to different biological processes16. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55802/55802fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/55802/55802fig6.jpg" /> 
Figure 6: Representative Extraction Examples of Various Tissue Types from Wild-type Arabidopsis thaliana. 
All the samples were extracted using 50 mg fresh weight from the indicated tissues. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/55802/55802fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>% Buffer A to Buffer B</td>
			<td></td>
		</tr>
		<tr>
			<td>0 to 1 min</td>
			<td>45% A&#160;</td>
			<td>Buffer A: 1% 1M NH4-Acetate, 0.1% acetic acid in UPLC MS grade water</td>
		</tr>
		<tr>
			<td>1 to 4 min&#160;</td>
			<td>Linear gradient from 45% A to 25% A</td>
			<td>Buffer B: 1% 1M NH4-Acetate, 0.1% acetic acid in acetonitrile/isopropanol 7:3, (v:v)</td>
		</tr>
		<tr>
			<td>4 to 12 min&#160;</td>
			<td>Linear gradient from 25% A to 11% A</td>
			<td>Flow rate 400 &#181;L/min</td>
		</tr>
		<tr>
			<td>12 to 15 min&#160;</td>
			<td>Linear gradient from 11% A to 0% A</td>
			<td>Injection volume 2 &#181;L</td>
		</tr>
		<tr>
			<td>15 to 19.5 min</td>
			<td>Wash the column for 4.5 min with 0% A</td>
			<td></td>
		</tr>
		<tr>
			<td>19.50 to 19.51 min</td>
			<td>Set back to 45% A&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>19.51 to 24 min</td>
			<td>Equilibrate with 45% A&#160;</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1: Gradient Parameters for RP-UPLC Separation of Lipids. RP, reversed phase. UPLC, ultra-performance liquid chromatography.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>% Buffer A to Buffer B</td>
			<td></td>
		</tr>
		<tr>
			<td>0 to 1 min</td>
			<td>99% A</td>
			<td>Buffer A: 0.1% formic acid in UPLC grade water</td>
		</tr>
		<tr>
			<td>1 to 11 min</td>
			<td>Linear gradient from 99% A to 60% A</td>
			<td>Buffer B: 0.1% formic acid in UPLC grad acetonitrile</td>
		</tr>
		<tr>
			<td>11 to 13 min</td>
			<td>Linear gradient from 60% A to 30% A</td>
			<td>Flow rate 400 &#181;L/min</td>
		</tr>
		<tr>
			<td>13 to 15 min</td>
			<td>Linear gradient from 30% A to 1% A</td>
			<td>Injection volume 2 &#181;L</td>
		</tr>
		<tr>
			<td>15 to 16 min</td>
			<td>Wash the column for 1 min with 1% A&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>16 to 17 min</td>
			<td>Linear gradient from1% A to 99% A</td>
			<td></td>
		</tr>
		<tr>
			<td>17 to 20 min</td>
			<td>Equilibrate for 3 min at 99% A&#160;</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2: Gradient Parameters for RP-UPLC Separation of Polar and Semi-polar Metabolites. RP, reversed phase. UPLC, ultra-performance liquid chromatography.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>% Buffer B to Buffer A</td>
			<td></td>
		</tr>
		<tr>
			<td>0 to 5 min</td>
			<td>Linear gradient from 0 to10%</td>
			<td>Buffer A: 0.1% formic acid in UPLC grade water</td>
		</tr>
		<tr>
			<td>5 to 80 min</td>
			<td>Linear gradient from 10% to 40%</td>
			<td>Buffer B: 0.1% formic acid in 60% UPLC grade acetonitrile</td>
		</tr>
		<tr>
			<td>80 to 85 min</td>
			<td>Linear gradient from 40% to 60%&#160;</td>
			<td>Flow rate 300 nL/min</td>
		</tr>
		<tr>
			<td>85 to 86 min</td>
			<td>Linear gradient from 60%&#160; to 95%&#160;</td>
			<td>Injection volume 5 &#181;L</td>
		</tr>
		<tr>
			<td>86 to 91 min</td>
			<td>Wash column for 5 min with 95%&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>91 to 92 min</td>
			<td>Linear gradient from 95%&#160; to 0%&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>93 to 110 min</td>
			<td>Equilibrate the column for 17 min at 0%</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3: Gradient Parameters for Nano-LC Separation of Peptides. LC, liquid chromatography. 
&#160;

Watch this Scientific Journal Video about A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample at JoVE.com

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

A protocol for comprehensive extraction of lipids, metabolites and proteins from biological tissues using one sample is presented.

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually ...

a-simple-fractionated-extraction-method-for-comprehensive-analysis

Research

JoVE Journal

Biochemistry

34.7K Views.  Max Planck Institute of Molecular Plant Physiology, Golm, Germany. A protocol for comprehensive extraction of lipids, metabolites and proteins from biological tissues using one sample is presented.

Video: A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

Method for Identifying Small Molecule Inhibitors of the Protein-protein Interaction Between HCN1 and TRIP8b

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the excitability of neurons. The subcellular distribution of these channels in pyramidal neurons of hippocampal area CA1 is regulated by tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b), an auxiliary subunit. Genetic knockout of HCN pore forming subunits or TRIP8b, both lead to an increase in antidepressant-like behavior, suggesting that limiting the function of HCN channels may be useful as a treatment for Major Depressive Disorder (MDD). Despite significant therapeutic interest, HCN channels are also expressed in the heart, where they regulate rhythmicity. To circumvent off-target issues associated with blocking cardiac HCN channels, our lab has recently proposed targeting the protein-protein interaction between HCN and TRIP8b in order to specifically disrupt HCN channel function in the brain. TRIP8b binds to HCN pore forming subunits at two distinct interaction sites, although here the focus is on the interaction between the tetratricopeptide repeat (TPR) domains of TRIP8b and the C terminal tail of HCN1. In this protocol, an expanded description of a method for purifying TRIP8b and executing a high throughput screen to identify small molecule inhibitors of the interaction between HCN and TRIP8b, is described. The method for high throughput screening utilizes a Fluorescence Polarization (FP) -based assay to monitor the binding of a large TRIP8b fragment to a fluorophore-tagged eleven amino acid peptide corresponding to the HCN1 C terminal tail. This method allows &#39;hit&#39; compounds to be identified based on the change in the polarization of emitted light. Validation assays are then performed to ensure that &#39;hit&#39; compounds are not artifactual.

The interaction between HCN channels and their auxiliary subunit has been identified as a therapeutic target in Major Depressive Disorder. Here, a fluorescence polarization-based method for identifying small molecule inhibitors of this protein-protein interaction, is presented.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the ...

A Protein Preparation Method for the High-throughput Identification of Proteins Interacting with a Nuclear Cofactor Using LC-MS/MS Analysis

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in regulating transcription. These include the direct interaction with transcription factors, the covalent modification of histones and other proteins, and the occasional chromatin conformation alteration. Accordingly, establishing relatively quick methods for identifying proteins that interact within this network is crucial to enhancing our understanding of the underlying regulatory mechanisms. LC-MS/MS-mediated protein binding partner identification is a validated technique used to analyze protein-protein interactions. By immunoprecipitating a previously-identified member of a protein complex with an antibody (occasionally with an antibody for a tagged protein), it is possible to identify its unknown protein interactions via mass spectrometry analysis. Here, we present a method of protein preparation for the LC-MS/MS-mediated high-throughput identification of protein interactions involving nuclear cofactors and their binding partners. This method allows for a better understanding of the transcriptional regulatory mechanisms of the targeted nuclear factors.

We have established a method for the purification of coregulatory interaction proteins using the LC-MS/MS system.

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in ...

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 &#197; structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

This paper presents a protocol for processing cryo-EM images using the software suite SPHIRE. The present protocol can be applied for nearly all single particle EM projects that target near-atomic resolution.

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single ...

Optimized Protocol for the Extraction of Proteins from the Human Mitral Valve

Analysis of the cellular proteome can help to elucidate the molecular mechanisms underlying diseases due to the development of technologies that permit the large-scale identification and quantification of the proteins present in complex biological systems.The knowledge gained from a proteomic approach can potentially lead to a better understanding of the pathogenic mechanisms underlying diseases, allowing for the identification of novel diagnostic and prognostic disease markers, and, hopefully, of therapeutic targets. However, the cardiac mitral valve represents a very challenging sample for proteomic analysis because of the low cellularity in proteoglycan and collagen-enriched extracellular matrix. This makes it challenging to extract proteins for a global proteomic analysis. This work describes a protocol that is compatible with subsequent protein analysis, such as quantitative proteomics and immunoblotting. This can allow for the correlation of data concerning protein expression with data on quantitative mRNA expression and non-quantitative immunohistochemical analysis. Indeed, these approaches, when performed together, will lead to a more comprehensive understanding of the molecular mechanisms underlying diseases, from mRNA to post-translational protein modification. Thus, this method can be relevant to researchers interested in the study of cardiac valve physiopathology.

The protein composition of the human mitral valve is still partially unknown, because its analysis is complicated by low cellularity and therefore by low protein biosynthesis. This work provides a protocol to efficiently extract protein for the analysis of the mitral valve proteome.

Analysis of the cellular proteome can help to elucidate the molecular mechanisms underlying diseases due to the development of technologies that permit ...

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages for identifying biologically active chemical structures that can modify complex phenotypes such as lifespan. Described here is a simple protocol for producing hundreds of 96-well culture plates with fairly consistent numbers of C. elegans in each well. Next, we specified how to use these cultures to screen thousands of chemicals for effects on the lifespan of the nematode C. elegans. This protocol makes use of temperature sensitive sterile strains, agar plate conditions, and simple animal handling to facilitate the rapid and high throughput production of synchronized animal cultures for screening.

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Here we describe a simple protocol for rapidly producing hundreds of nematode growth media agar, 96-well culture plates with consistent numbers of Caenorhhabditis elegans per well. These cultures are useful for the phenotypic screening of whole organisms. We focus here on using these cultures to screen chemicals for pro-longevity effects.

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages ...

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view of the impact of a disease, treatment, or other condition on the proteome as a whole. This report provides a detailed protocol for the extraction, quantification, precipitation, digestion, labeling, and subsequent data analysis of protein samples. Our optimized TMT labeling protocol requires a lower tag-label concentration and achieves consistently reliable data. We have used this protocol to evaluate protein expression profiles in a variety of mouse tissues (i.e., heart, skeletal muscle, and brain) as well as cells cultured in vitro. In addition, we demonstrate how to evaluate thousands of proteins from the resulting dataset.

We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...

Transverse Sectioning of Mature Rice (Oryza sativa L.) Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support

Starch granules (SGs) exhibit different morphologies depending on the plant species, especially in the endosperm of the Poaceae family. Endosperm phenotyping can be used to classify genotypes based on SG morphotype using scanning electron microscopic (SEM) analysis. SGs can be visualized using SEM by slicing through the kernel (pericarp, aleurone layers, and endosperm) and exposing the organellar contents. Current methods require the rice kernel to be embedded in plastic resin and sectioned using a microtome or embedded in a truncated pipette tip and sectioned by hand using a razor blade. The former method requires specialized equipment and is time-consuming, while the latter introduces a new host of problems depending on rice genotype. Chalky rice varieties, particularly, pose a problem for this type of sectioning due to the friable nature of their endosperm tissue. Presented here is a technique for preparing translucent and chalky rice kernel sections for microscopy, requiring only pipette tips and a scalpel blade. Preparing the sections within the confines of a pipette tip prevents rice kernel endosperm from shattering (for translucent or &#8216;vitreous&#8217; phenotypes) and crumbling (for chalky phenotypes). Using this technique, endosperm cell patterning and the structure of intact SGs can be observed.

Transverse Sectioning of Mature Rice Oryza sativa L. Kernels for Scanning Electron Microscopy Imaging Using Pipette Tips as Immobilization Support

This protocol allows for the preparation of transverse sections of cereal seeds (e.g., rice) for the analysis of endosperm and starch granule morphology using scanning electron microscopy.

Starch granules (SGs) exhibit different morphologies depending on the plant species, especially in the endosperm of the Poaceae family. Endosperm ...

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk population methods (Northern blot, PCR, and RNAseq) that measure mRNA levels lack the ability to provide information on cell-to-cell variation in responses. Precise single cell and allelic visualization and quantification is possible via single molecule RNA fluorescence in situ hybridization (smFISH). RNA-FISH is performed by hybridizing target RNAs with labeled oligonucleotide probes. These can be imaged in medium/high throughput modalities, and, through image analysis pipelines, provide quantitative data on both mature and nascent RNAs, all at the single cell level. The fixation, permeabilization, hybridization and imaging steps have been optimized in the lab over many years using the model system described herein, which results in successful and robust single cell analysis of smFISH labeling. The main goal with sample preparation and processing is to produce high quality images characterized by a high signal-to-noise ratio to reduce false positives and provide data that are more accurate. Here, we present a protocol describing the pipeline from sample preparation to data analysis in conjunction with suggestions and optimization steps to tailor to specific samples.&#160;

Single molecule RNA fluorescence in situ hybridization (smFISH) is a method to accurately quantify levels and localization of specific RNAs at the single cell level. Here, we report our validated lab protocols for wet-bench processing, imaging and image analysis for single cell quantification of specific RNAs.

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk ...

Removal and Replacement of Endogenous Ligands from Lipid-Bound Proteins and Allergens

Many major allergens bind to hydrophobic lipid-like molecules, including Mus m 1, Bet v 1, Der p 2, and Fel d 1. These ligands are strongly retained and have the potential to influence the sensitization process either through directly stimulating the immune system or altering the biophysical properties of the allergenic protein. In order to control for these variables, techniques are required for the removal of endogenously bound ligands and, if necessary, replacement with lipids of known composition. The cockroach allergen Bla g 1 encloses a large hydrophobic cavity which binds a heterogeneous mixture of endogenous lipids when purified using traditional techniques. Here, we describe a method through which these lipids are removed using reverse-phase HPLC followed by thermal annealing to yield Bla g 1 in either its Apo-form or reloaded with a user-defined mixture of fatty acid or phospholipid cargoes. Coupling this protocol with biochemical assays reveal that fatty acid cargoes significantly alter the thermostability and proteolytic resistance of Bla g 1, with downstream implications for the rate of T-cell epitope generation and allergenicity. These results highlight the importance of lipid removal/reloading protocols such as the one described herein when studying allergens from both recombinant and natural sources. The protocol is generalizable to other allergen families including lipocalins (Mus m 1), PR-10 (Bet v 1), MD-2 (Der p 2) and Uteroglobin (Fel d 1), providing a valuable tool to study the role of lipids in the allergic response.

This protocol describes the removal of endogenous lipids from allergens, and their replacement with user-specified ligands through reverse-phase HPLC coupled with thermal annealing. 31P-NMR and circular dichroism allow for the rapid confirmation of ligand removal/loading, and the recovery of native allergen structure.

Many major allergens bind to hydrophobic lipid-like molecules, including Mus m 1, Bet v 1, Der p 2, and Fel d 1. These ligands are strongly retained and ...

Sample Preparation using a Lipid Monolayer Method for Electron Crystallographic Studies

Electron crystallography is a powerful tool for high-resolution structure determination. Macromolecules such as soluble or membrane proteins can be grown into highly ordered two-dimensional (2D) crystals under favorable conditions. The quality of the grown 2D crystals is crucial to the resolution of the final reconstruction via 2D image processing. Over the years, lipid monolayers have been used as a supporting layer to foster the 2D crystallization of peripheral membrane proteins as well as soluble proteins. This method can also be applied to 2D crystallization of integral membrane proteins but requires more extensive empirical investigation to determine detergent and dialysis conditions to promote partitioning to the monolayer. A lipid monolayer forms at the air-water interface such that the polar lipid head groups remain hydrated in the aqueous phase and the non-polar, acyl chains, tails partition into the air, breaking the surface tension and flattening the water surface. The charged nature or distinctive chemical moieties of the head groups provide affinity for proteins in solution, promoting binding for 2D array formation. A newly formed monolayer with the 2D array can be readily transfer into an electron microscope (EM) on a carbon-coated copper grid used to lift and support the crystalline array. In this work, we describe a lipid monolayer methodology for cryogenic electron microscopic (cryo-EM) imaging.

Lipid monolayers have been used as a foundation for forming two-dimensional (2D) protein crystals for structural studies for decades. They are stable at the air-water interface and can serve as a thin supporting material for electron imaging. Here we present the proven steps on preparing lipid monolayers for biological studies.

Electron crystallography is a powerful tool for high-resolution structure determination. Macromolecules such as soluble or membrane proteins can be grown ...